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Relational Embedding for Few-Shot Classification
[article]
2021
arXiv
pre-print
We propose to address the problem of few-shot classification by meta-learning "what to observe" and "where to attend" in a relational perspective. Our method leverages relational patterns within and between images via self-correlational representation (SCR) and cross-correlational attention (CCA). Within each image, the SCR module transforms a base feature map into a self-correlation tensor and learns to extract structural patterns from the tensor. Between the images, the CCA module computes
arXiv:2108.09666v1
fatcat:2rme4mbmgrfqdmaf7oubxqejaq